Investigations of natural products have, historically, been extremely challenging due to the complexity of most natural product sources. Attempts at accurately describing the constitution of complex these samples remains a challenge, despite advancements in analytical technologies. Although no singular process exists to solve this issue, combinations of data from orthogonal techniques can be combined to improve the annotation of a mixture. Utilizing NMR as an analytical platform, an approach was developed that integrates orthogonal information from HSQC and TOCSY experiments, enabling the construction of spin system features that can be used for identification and description of individual constituents in a mixture. Using this approach, it is possible to network related constituents across multiple samples to visualize relationships of the features aiding in further hypothesis driven investigations. Approaching this problem from a metabolomics perspective takes advantage of already existing natural product workflows that may result in a large number of collections of source organisms. Further evaluation of these networks show promise in accurately describing components in enough detail to not only track their prevalence across extracts, but provide the investigators with enough detail to know the molecular scaffold and structural features of these metabolites prior to extensive chromatographic separation. Future efforts and applications included in this proposal aim to connect the structural information afforded by this NMR system to structurally determined phenomena in other analytical platforms, predominantly with MS.